Hello there!
I'm Sai.

I'm currently a PhD Candidate in Computer Science and Engineering at the University of
Michigan.

My research focuses on how humans and machines can train each other to help the other become
better at doing tasks that otherwise can't be solved alone. I'm particularly interested in
crowdsourced robotics, especially how we can leverage human intelligence (in the form of
crowdsourcing**) to help bridge robots' knowledge gaps.

New(s)

05/2019: Summer internship at Microsoft Research

Over the moon to be interning at Microsoft Research for Summer 2019! I will be working with and learning from my mentors Justin Cranshaw and Pamela Bhattacharya. We'll be looking at what it means for relationship management if we had an intelligent assistant (like calendar.help) to help with this task. Holla @ me if you'll be in Seattle anytime in the summer :)

05/2019: Paper at ACL

Our submission entitled "A Large-Scale Corpus for Conversation Disentanglement" has been accepted at ACL 2019! (This work was led by Jonathan Kummerfeld, our lab's amazing postdoc.)

01/2019: Instructor for EECS 493

I'm an Instructor of Record for EECS 493: User Interface Development for the Winter 2019 semester! This senior-level course presents design methods, UI abstractions, and practical examples of tools and languages commonly used in (web) UI development---all grounded within core HCI principles.

11/2018: UMich CSE Graduate Honors Competition finalist

I was the "Interactive Systems" representative for this year's honors competition! I talked about my work in creating robust, reliable, and deployable systems by leveraging crowdsourcing to create new training processes in order to solve real-world problems. Click here for a press article covering this event.

Towards Crowd-Assisted Data Mining

Current Projects

Generalized Object Recognition for Robots

Automated computer vision approaches to detecting objects in a scene require lots of training data; however, these automated approaches can often fail in real-world settings since objects can be occluded, broken, deformed, or in different configurations than those that were used to train the classifiers.

Our goal in this project is to go from limited knowledge about how an object can be in different configurations to an expanded knowledge---all from non-expert crowd worker manipulations of initial object configurations.

EURECA

How can we create reliable and more robust robot perception (3D vision)? We introduce EURECA, a web-based, mixed-initiative, collaborative annotation tool. EURECA lets groups of people (such as crowds on the internet) collectively ground natural language references to objects in 3D scenes in real time.

Our approach requires no prior human or machine training and can in fact generate training data. We target settings where automated systems will have already failed, so this approach is still complementary to existing CV approaches if we have access to preprocessed clusters, labels, etc. The ultimate goal is to help robots “see” their environments better.

Collective Crowd Memory

Crowd-powered conversational systems---in which ever-changing groups of remote human workers collectively hold a conversation with end users---can help bootstrap automated dialog systems by generating training data in real scenarios and succeed where well-trained automated approaches fail. However, since no one worker is present during all sessions, these systems fail to remember all relevant information from interactions that span multiple sessions, leading over time to the loss of conversational context.

We introduce Mnemo, a crowd-powered dialog system plug-in that uses collective processes and automated support to maintain a “collective crowd memory” of user conversations through crowd-generated facts, which workers predict would be important when a similar topic is again discussed in the future by a given user.

LegionTools

LegionTools (LT) is a software tool that allows users to easily recruit and route Amazon Mechanical Turk workers for synchronous realtime crowdsourcing tasks. It was first developed by researchers from HCI at the University of Rochester and is now being maintained by researchers from the Crowds + Machines (CROMA) Lab at the University of Michigan.

LegionTools makes it easier for researchers (and other users) to interface with Amazon Mechanical Turk for their crowdsourcing tasks. LT allows users to create HITs with richer options, as well as makes it easier to keep track of posted HITs, bonus payments, etc. LT also enables real time crowdsourcing applications to interface with AMT and takes care of recruiting, retaining, and routing workers to specific tasks.